VAST v3.0 is out. This release brings some major updates to the the VAST language, making it easy to write down dataflow pipelines that filter, reshape, aggregate, and enrich security event data. Think of VAST as security data pipelines plus open storage engine.
VAST v2.4.1 improves the performance of queries when VAST is under high load, and significantly reduces the time to first result for queries with a low selectivity.
VAST v2.3.1 is now available. This small bugfix release
addresses an issue where compaction would hang if encountering
invalid partitions that were produced by older versions of VAST when a large
max-partition-size was set in combination with badly compressible input data.
VAST v2.3 is now available, which introduces an automatic data defragmentation capability.
VAST v2.1 is out! This release comes with a particular focus on performance and reducing the size of VAST databases. It brings a new utility for optimizing databases in production, allowing existing deployments to take full advantage of the improvements after upgrading.
VAST bets on Apache Arrow as the open interface to structured data. By "bet," we mean that VAST does not work without Arrow. And we are not alone. Influx's IOx, DataDog's Husky, Anyscale's Ray, TensorBase, and others committed themselves to making Arrow a corner stone of their system architecture. For us, Arrow was not always a required dependency. We shifted to a tighter integration over the years as the Arrow ecosystem matured. In this blog post we explain our journey of becoming an Arrow-native engine.
Dear community, we are excited to announce VAST v2.0, bringing faster execution of bulk-submitted queries, improved tunability of index structures, and new configurability through environment variables.